Bayesian Inductive Logic Programming

Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learning and Computational Learning Theory, ILP is based on lockstep development of Theory, Implementations and Applications. ILP systems have successful applications in the learning of structure-activity rules for drug design, semantic grammars rules, finite element mesh design rules and rules for prediction of protein structure and mutagenic molecules. The strong applications in ILP can be contrasted with relatively weak PAC-learning results (even highlyrestricted forms of logic programs are known to be prediction-hard). It has been recently argued that the mismatch is due to distributional assumptions made in application domains. These assumptions can be modelled as a Bayesian prior probability representing subjective degrees of belief. Other authors have argued for the use of Bayesian prior distributions for rea...

• Type: Conference
• Year: 1994
• Where: COLT
• Authors: Stephen Muggleton
• Keywords: Bayesian Prior, Bayesian Prior Distributions, COLT 1994, Computational Learning Theory, Machine Learning